Abstract:

A tremendous need exists for intelligent agents that can be created and edited without resorting to intensive knowledge engineering and programming, and which exhibit believable and variable behavior in the training contexts in which they are deployed. This proposal describes a novel method for creating and editing intelligent agents' behavior based on using instance-based modeling and statistical learning methods that learn from the example of a person interacting in a virtual environment. These methods, which leverage structured knowledge in a hybrid symbolic-subsymbolic approach, support automatic incorporation of assessment feedback directly from the interface into an agent, allowing a domain expert to interact with an agent in a closed feedback loop through a participation in a virtual environment, instead of through lengthy reprogramming by a knowledge engineering expert.